A vision system for symbolic interpretation of dynamic scenes using arsom

We describe an artificial high-level vision system for the symbolic interpretation of data coming from a video camera that acquires the image sequences of moving scenes. The system is based on ARSOM neural networks that learn to generate the perception-grounded predicates obtained by image sequences. The ARSOM neural networks also provide a three-dimensional estimation of the movements of the relevant objects in the scene. The vision system has been employed in two scenarios: the monitoring of a robotic arm suitable for space operations, and the surveillance of an electronic data processing (EDP) center.     

A Study of Hybrid Neural Network Approaches and the Effects of Missing Data on Traffic Forecasting

In this paper we present an application of hybrid neural network approaches and an assessment of the effects of missing data on motorway traffic flow forecasting. Two hybrid approaches are developed using a Self-Organising Map (SOM) to initially classify traffic into different states. The first hybrid approach includes four Auto-Regressive Integrated Moving Average (ARIMA) models, whilst the second uses two Multi-Layer Perception (MLP) models. It was found that the SOM/ARIMA hybrid approach out-performs all individual ARIMA models, whilst the SOM/MLP hybrid approach achieves superior forecasting performance to all models used in this study, including three na?ve models. The effects of different proportions of missing data on Neural Network (NN) performance when forecasting traffic flow are assessed and several initial substitution options to replace missing data are discussed. Over-all, it is shown that ARIMA models are more sensitive to the percentage of missing data than neural networks in this context.       

Improved PLSOM algorithm

The original Parameter-Less Self-Organising Map (PLSOM) algorithm was introduced as a solution to the problems the Self-Organising Map (SOM) encounters when dealing with certain types of mapping tasks. Unfortunately the PLSOM suffers from over-sensitivity to outliers and over-reliance on the initial weight distribution. The PLSOM2 algorithm is introduced to address these problems with the PLSOM. PLSOM2 is able to cope well with outliers without exhibiting the problems associated with the standard PLSOM algorithm. The PLSOM2 requires very little computational overhead compared to the standard PLSOM, thanks to an efficient method of approximating the diameter of the inputs, and does not rely on a priori knowledge of the training input space. This paper provides a discussion of the reasoning behind the PLSOM2 and experimental results showing its effectiveness for mapping tasks.     

Visual feature analysis by the self-organising maps

The Self-Organising Map (SOM) is an Artificial Neural Network (ANN) model consisting of a regular grid of processing units. A model of some multidimensional observation, e.g. a class of digital images, is associated with each unit. The map attempts to represent all the available observations using a restricted set of models. In unsupervised learning, the models become ordered on the grid so that similar models are close to each other. We review here the objective functions and learning rules related to the SOM, starting from vector coding based on a Euclidean metric and extending the theory of arbitrary metrics and to a subspace formalism, in which each SOM unit represents a subspace of the observation space. It is shown that this Adaptive-Subspace SOM (ASSOM) is able to create sets of wavelet- and Gabor-type filters when randomly displaced or moving input patterns are used as training data. No analytical functional form for these filters is thereby postulated. The same kind of adaptive system can create many other kinds of invariant visual filters, like rotation or scale-invariant filters, if there exist corresponding transformations in the training data. The ASSOM system can act as a learning feature-extraction stage for pattern recognisers, being able to adapt to arbitrary sensory environments. We then show that the invariant Gabor features can be effectively used in face recognition, whereby the sets of Gabor filter outputs are coded with the SOM and a face is represented by the histogram over the SOM units.     

Active audition using the parameter-less self-organising map

This paper presents a novel method for enabling a robot to determine the position of a sound source in three dimensions using just two microphones and interaction with its environment. The method uses the Parameter-Less Self-Organising Map (PLSOM) algorithm and Reinforcement Learning (RL) to achieve rapid, accurate response. We also introduce a method for directional filtering using the PLSOM. The presented system is compared to a similar system to evaluate its performance.

Probability estimation algorithms for self-validating sensors

Three alternative approaches are investigated for probability estimation for use in a self-validating sensor. The three methods are Stochastic Approximation (SA), a Reduced Bias Estimate (RBE) of this same approach and a method based on the Bayesian Self-Organising Map using Gaussian Kernels (GK). Simulation studies show that the GK-based method gives superior results when compared to the RBE algorithm. It has also been demonstrated that the GK method is more computationally efficient and requires storage space for fewer variables. The techniques are demonstrated using data from a thermocouple sensor experiencing a change in time constant.     

Texture segmentation using pyramidal Gabor functions and self-organising feature maps

This paper presents texture segmentation realised with image treatment methods and an artificial neural network model. Gabor oriented filters are used to extract frequential texture features and Self-Organising Feature Maps are used to group and interpolate these features. In order to decrease the number of filters, we use a pyramidal multiresolution method of image representation. We intend to build an architecture inspired by the early stages of the visual cortex, while making local frequential analysis of the images, which must be able to segment different textured images.     

Landscapes of Naïve Bayes classifiers

The performance of the Naïve Bayes classifier (NB) is of interest to many researchers. The desire to improve upon the apparent good performance of NB while maintaining its efficiency and simplicity is demonstrated by the variety of adaptations to NB in the literature. This study takes a look at 37 such adaptations. The idea is to give a qualitative overview of the adaptations rather than a quantitative analysis of their performance. Landscapes are produced using Sammon mapping, Principal Component Analysis (PCA) and Self-Organising feature Maps (SOM). Based on these, the methods are split into five main groups—tree structures, feature selection, space transformation, Bayesian networks and joint features. The landscapes can also be used for placing any new variant of NB to obtain its nearest neighbours as an aid for comparison studies.     

Using SOM in the performance monitoring of the emergency call-taking system

The collaborative emergency call-taking information system in the Czech Republic forms a network of cooperating emergency call centres processing emergency calls to the European 112 emergency number. Large amounts of various incident records are stored in its databases. The data can be used for mining spatial and temporal anomalies, as well as for the monitoring and analysis of the performance of the emergency call-taking system. In this paper we describe a method for knowledge discovery and visualisation targeted at the performance analysis of the system with respect to the organisation of the emergency call-taking information system and its data characteristics. The method is based on the Kohonen Self-Organising Map (SOM) algorithm and its extension, the Growing Grid algorithm.     

Identification of Residential Property Sub-Markets using Evolutionary and Neural Computing Techniques

This paper expands on previous work considering methods of stratifying property data in order to enhance its susceptibility to modelling for mortgage value estimation. Previous work [1] considered a clustering approach using a Kohonen Self-Organising Map (SOM) to stratify the training data prior to training a suite of MLPs. Although the results were encouraging, the approach suffers from its estimation of trainability post-clustering. The following method ameliorates the approach by replacing the static clustering step with a dynamic genetic algorithm implementation. The results show a healthy improvement in accuracy over the non-stratified approach, and a more consistent level of accuracy compared with the Kohonen SOM approach. The paper concludes by analysing the underlying content of the derived stratas, thus providing a ‘human readable’ element to the approach that enhances its potential for acceptance by valuation institutions for as a complementary technique to traditional valuation methods.